This repository contains a collection of R scripts developed for analyzing single-cell transcriptomics data. It emphasizes identifying cell-type-specific gene expression patterns, conducting dimensionality reduction analyses, and visualizing complex intersections of differentially expressed genes (DEGs).
- Differential Expression Analysis: Identify and visualize DEGs across multiple cell types.
- Principal Component Analysis (PCA): Reduce data dimensionality and explore gene expression variability across cell populations.
- Gene Ontology (GO) Enrichment: Discover biological processes associated with cell-type-specific DEGs.
- Intersection Visualization: Generate UpSet plots to visualize shared and unique gene expression patterns among cell types.
| Script/File | Description |
|---|---|
all_DEGs_plots.R |
Generates plots of DEGs across various cell types. |
pca_cell_type_only.R |
Performs PCA specifically focused on cell-type differences. |
topGo_unique_DEGs_Celltype.R |
Conducts GO enrichment analysis using topGO. |
upset_overall.R |
Creates UpSet plots for DEG intersections among cell types. |
ASC_scripts.zip |
Additional scripts/data related to adipose-derived stem cells (ASCs). |
- R (version 4.0 or later recommended)
- Required packages:
topGO,ggplot2,UpSetR,Seurat, and other dependencies as specified within scripts.
Clone the repository and ensure all dependencies are installed. Execute scripts within an R environment or IDE (e.g., RStudio).
git clone https://github.com/Zubair2021/ARV_Cell_Transcriptomics_2024.git
cd ARV_Cell_Transcriptomics_2024Run the desired scripts directly within R:
source("all_DEGs_plots.R")
source("pca_cell_type_only.R")
source("topGo_unique_DEGs_Celltype.R")
source("upset_overall.R")Contributions to improve the analysis scripts and add additional features are welcome. Please fork the repository and submit a pull request.